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| From | clivelists@googlemail.com |
| To | statalist@hsphsun2.harvard.edu |
| Subject | Re: st: RE: Mean test in a Likert Scale |
| Date | Thu, 6 Sep 2012 09:54:10 +0000 |
That's actually a Type I error... C -----Original Message----- From: Yuval Arbel <yuval.arbel@gmail.com> Sender: owner-statalist@hsphsun2.harvard.edu Date: Thu, 6 Sep 2012 12:06:52 To: <statalist@hsphsun2.harvard.edu> Reply-To: statalist@hsphsun2.harvard.eduSubject: Re: st: RE: Mean test in a Likert Scale Maarten, am I correct by saying you imply a second type error, namely a rejection of the restriction in the case you should not have rejected it? On Thu, Sep 6, 2012 at 11:55 AM, Yuval Arbel <yuval.arbel@gmail.com> wrote: > Maarteen, I didn't quite follow the statement you made regarding free > lunch and restricted models. I thought the less restrictive the model > is the more powerful it is. It is well known, for example, that > estimation of unrestricted models always yield higher R-squares and > higher log-likelihood - so the LR statistics (or F-Statistics) for > testing the validity of a restriction imposed on a model have to be > positive > > On Mon, Sep 3, 2012 at 11:21 PM, Cameron McIntosh <cnm100@hotmail.com> wrote: >> This paper might also be of interest: >> >> Wu, C.-H. (2007). An empirical study on the transformation of likert-scale data to numerical scores. Applied Mathematical Sciences, 1(58), 2851-2862. >> http://www.m-hikari.com/ams/ams-password-2007/ams-password57-60-2007/wuchienhoAMS57-60-2007.pdf >> >> Cam >> >>> Date: Mon, 3 Sep 2012 15:44:30 -0500 >>> To: statalist@hsphsun2.harvard.edu; statalist@hsphsun2.harvard.edu >>> From: richardwilliams.ndu@gmail.com >>> Subject: Re: st: RE: Mean test in a Likert Scale >>> >>> At 11:00 AM 9/3/2012, Maarten Buis wrote: >>> >On Mon, Sep 3, 2012 at 4:54 PM, Yuval Arbel wrote: >>> > > Nick and Maarten, Note, that Kmenta's message is to prefer models with >>> > > less restrictions. >>> > >>> >As always, there is no such thing as a free lunch. Less restrictions >>> >typically cost statistical power, and if the restriction works well >>> >for a particular applications, not using it will be a waste. Moreover, >>> >such statements are in practice used to prefer models with less known >>> >restrictions over models with well known restrictions. For example, I >>> >have seen it used to prefer an -oprobit- over an -ologit- because >>> >-ologit- implies the proportional odds assumption and -oprobit- >>> >implies an equivalent assumption with a less memorable name. >>> >>> I had a fairly prominent econometrician make that argument to me >>> once. My response was that both ologit and oprobit require what has >>> been called the parallel lines or parallel regressions assumption to >>> be met. It just so happens that, with ologit, if parallel lines holds >>> then proportional odds will hold too. But it isn't like ologit has an >>> additional hurdle to clear; it is just that if it clears the parallel >>> lines hurdle, it simultaneously clears the proportional odds hurdle too. >>> >>> >>> > > Moreover, are you suggesting we can deal in the same manner with >>> > > quantitative values and ordinal variables? if our independent >>> > > variables are what subjects marked on a questionnaire on a scale >>> > > between 1 to 5 is the statistical treatment within a regression >>> > > analysis framework should be identical to an independent variable >>> > > measured in US dollars? >>> > >>> >No, all I am saying is that I do not rule out that there exists an >>> >application where treating a ordinal variable as having a linear >>> >effect works well enough and that it is worth checking whether that is >>> >the case, as you can safe a lot of power that way. Moreover, an amount >>> >in dollars may not be as cardinal as one might hope; often respondents >>> >round their answers considerably even if asked to provide exact >>> >answers. >>> >>> Maybe this has already been mentioned, but pages 421-422 of Long & >>> Freese (2006) show how to test whether an ordinal independent >>> variable can be treated as though it were interval. See >>> >>> http://www.stata.com/bookstore/regression-models-categorical-dependent-variables/index.html >>> >>> ------------------------------------------- >>> Richard Williams, Notre Dame Dept of Sociology >>> OFFICE: (574)631-6668, (574)631-6463 >>> HOME: (574)289-5227 >>> EMAIL: Richard.A.Williams.5@ND.Edu >>> WWW: http://www.nd.edu/~rwilliam >>> >>> * >>> * For searches and help try: >>> * http://www.stata.com/help.cgi?search >>> * http://www.stata.com/support/statalist/faq >>> * http://www.ats.ucla.edu/stat/stata/ >> >> * >> * For searches and help try: >> * http://www.stata.com/help.cgi?search >> * http://www.stata.com/support/statalist/faq >> * http://www.ats.ucla.edu/stat/stata/ > > > > -- > Dr. Yuval Arbel > School of Business > Carmel Academic Center > 4 Shaar Palmer Street, > Haifa 33031, Israel > e-mail1: yuval.arbel@carmel.ac.il > e-mail2: yuval.arbel@gmail.com -- Dr. Yuval Arbel School of Business Carmel Academic Center 4 Shaar Palmer Street, Haifa 33031, Israel e-mail1: yuval.arbel@carmel.ac.il e-mail2: yuval.arbel@gmail.com * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/